def fit(self, data, task_indexes):
performance = []
for task_idx, task in enumerate(getattr(data, task_indexes)):
x = data.features_tr[task]
y = data.labels_tr[task]
n_points, n_dims = x.shape
best_perf = np.Inf
for step_idx, step_size in enumerate(self.step_size_range):
curr_untranslated_weights = np.zeros(n_dims)
curr_weights = curr_untranslated_weights + self.fixed_bias
all_weight_vectors = []
all_losses = []
shuffled_indexes = list(range(n_points))
np.random.shuffle(shuffled_indexes)
for iteration, curr_point_idx in enumerate(shuffled_indexes):
prev_untranslated_weights = curr_untranslated_weights
prev_weights = curr_weights
# receive a new datapoint
curr_x = x[curr_point_idx, :]
curr_y = y[curr_point_idx]
# compute the gradient
subgrad = subgradient(curr_x,
curr_y,
prev_weights,
loss_name='absolute')
full_gradient = subgrad * curr_x
# update weight vector
curr_untranslated_weights = prev_untranslated_weights - step_size * full_gradient
curr_weights = curr_untranslated_weights + self.fixed_bias
all_weight_vectors.append(curr_weights)
if len(all_weight_vectors) < 2:
final_w = curr_weights
else:
final_w = np.mean(all_weight_vectors, axis=0)
loss_thing = loss(x, y, final_w, loss_name='absolute')
all_losses.append(loss_thing)
curr_perf = loss(data.features_ts[task],
data.labels_ts[task],
final_w,
loss_name='absolute')
if curr_perf < best_perf:
best_perf = curr_perf
best_step = step_size
performance.append(best_perf)
print(performance)
def fit(self, data):
curr_meta_magnitude_betting_fraction = self.meta_magnitude_betting_fraction
curr_meta_magnitude_wealth = self.meta_magnitude_wealth
curr_meta_magnitude = curr_meta_magnitude_betting_fraction * curr_meta_magnitude_wealth
curr_meta_direction = np.zeros(data.features_tr[0].shape[1])
all_individual_cum_errors = []
best_mtl_performances = []
total_iter = 0
all_meta_parameters = []
all_final_weight_vectors = []
for task_iteration, task in enumerate(data.tr_task_indexes):
x = data.features_tr[task]
y = data.labels_tr[task]
task_iteration = task_iteration + 1
prev_meta_direction = curr_meta_direction
prev_meta_magnitude_betting_fraction = curr_meta_magnitude_betting_fraction
prev_meta_magnitude_wealth = curr_meta_magnitude_wealth
prev_meta_magnitude = curr_meta_magnitude
# update meta-parameter
meta_parameter = prev_meta_magnitude * prev_meta_direction
all_meta_parameters.append(meta_parameter)
# initialize the inner parameters
n_points, n_dims = x.shape
curr_inner_magnitude_betting_fraction = self.inner_magnitude_betting_fraction
curr_inner_magnitude_wealth = self.inner_magnitude_wealth
curr_inner_magnitude = curr_inner_magnitude_betting_fraction * curr_inner_magnitude_wealth
curr_inner_direction = np.zeros(x.shape[1])
temp_weight_vectors = []
all_gradients = []
shuffled_indexes = list(range(n_points))
# np.random.shuffle(shuffled_indexes)
for inner_iteration, curr_point_idx in enumerate(shuffled_indexes):
inner_iteration = inner_iteration + 1
prev_inner_direction = curr_inner_direction
prev_inner_magnitude_betting_fraction = curr_inner_magnitude_betting_fraction
prev_inner_magnitude_wealth = curr_inner_magnitude_wealth
prev_inner_magnitude = curr_inner_magnitude
# update inner weight vector
weight_vector = prev_inner_magnitude * prev_inner_direction + meta_parameter
temp_weight_vectors.append(weight_vector)
# receive a new datapoint
curr_x = x[curr_point_idx, :]
curr_y = y[curr_point_idx]
all_individual_cum_errors.append(loss(curr_x, curr_y, weight_vector, loss_name='absolute'))
# compute the gradient
subgrad = subgradient(curr_x, curr_y, weight_vector, loss_name='absolute')
full_gradient = subgrad * curr_x
all_gradients.append(full_gradient)
# define inner step size
inner_step_size = (1 / (self.L * self.R)) * np.sqrt(2 / inner_iteration)
# update inner direction
curr_inner_direction = l2_unit_ball_projection(prev_inner_direction - inner_step_size * full_gradient)
# update inner magnitude_wealth
curr_inner_magnitude_wealth = prev_inner_magnitude_wealth - 1 / (self.R * self.L) * full_gradient @ prev_inner_direction * prev_inner_magnitude
# update magnitude_betting_fraction
curr_inner_magnitude_betting_fraction = (1/inner_iteration) * ((inner_iteration-1) * prev_inner_magnitude_betting_fraction - (1/(self.L*self.R))*(full_gradient @ prev_inner_direction))
# update magnitude
curr_inner_magnitude = curr_inner_magnitude_betting_fraction * curr_inner_magnitude_wealth
# define total iteration
total_iter = total_iter + n_points
# compute meta-gradient
meta_gradient = np.sum(all_gradients, axis=0)
# define meta step size
meta_step_size = (1 / (self.L * self.R * n_points)) * np.sqrt(2 / task_iteration)
# update meta-direction
curr_meta_direction = l2_unit_ball_projection(prev_meta_direction - meta_step_size * meta_gradient)
# update meta-magnitude_wealth
curr_meta_magnitude_wealth = prev_meta_magnitude_wealth - (1 / (self.R * self.L * n_points)) * meta_gradient @ prev_meta_direction * prev_meta_magnitude
# update meta-magnitude_betting_fraction
curr_meta_magnitude_betting_fraction = (1/task_iteration) * ((task_iteration-1) * prev_meta_magnitude_betting_fraction - (1 / (self.L * self.R * n_points)) * (meta_gradient @ prev_meta_direction))
# update meta-magnitude
curr_meta_magnitude = curr_meta_magnitude_betting_fraction * curr_meta_magnitude_wealth
all_final_weight_vectors.append(np.mean(temp_weight_vectors, axis=0))
all_test_errors = []
for idx, curr_test_task in enumerate(data.tr_task_indexes[:task_iteration]):
all_test_errors.append(loss(data.features_ts[curr_test_task], data.labels_ts[curr_test_task], all_final_weight_vectors[idx], loss_name='absolute'))
best_mtl_performances.append(np.nanmean(all_test_errors))
self.all_meta_parameters = all_meta_parameters
return best_mtl_performances, pd.DataFrame(all_individual_cum_errors).rolling(window=10 ** 10, min_periods=1).mean().values.ravel()
def fit(self, data):
best_perf = np.Inf
for _, inner_step_size in enumerate(self.inner_step_size_range):
for _, meta_step_size in enumerate(self.meta_step_size_range):
all_individual_cum_errors = []
all_mtl_performances = []
all_final_weight_vectors = []
curr_metaparameter = np.zeros(data.features_tr[0].shape[1])
for task_iteration, task in enumerate(data.tr_task_indexes):
x = data.features_tr[task]
y = data.labels_tr[task]
task_iteration = task_iteration + 1
# initialize the inner parameters
n_points, n_dims = x.shape
curr_untranslated_weights = np.zeros(n_dims)
temp_weight_vectors = []
shuffled_indexes = list(range(n_points))
# np.random.shuffle(shuffled_indexes)
for inner_iteration, curr_point_idx in enumerate(
shuffled_indexes):
prev_untranslated_weights = curr_untranslated_weights
prev_metaparameter = curr_metaparameter
# update inner weight vector
curr_weights = curr_untranslated_weights + curr_metaparameter
temp_weight_vectors.append(curr_weights)
# receive a new datapoint
curr_x = x[curr_point_idx, :]
curr_y = y[curr_point_idx]
all_individual_cum_errors.append(
loss(curr_x,
curr_y,
curr_weights,
loss_name='absolute'))
# compute the gradient
subgrad = subgradient(curr_x,
curr_y,
curr_weights,
loss_name='absolute')
full_gradient = subgrad * curr_x
# update metaparameters
curr_metaparameter = prev_metaparameter - meta_step_size * full_gradient
# update the untranslated weights
curr_untranslated_weights = prev_untranslated_weights - inner_step_size * full_gradient
all_final_weight_vectors.append(
np.mean(temp_weight_vectors, axis=0))
all_test_errors = []
for idx, curr_test_task in enumerate(
data.tr_task_indexes[:task_iteration]):
all_test_errors.append(
loss(data.features_ts[curr_test_task],
data.labels_ts[curr_test_task],
all_final_weight_vectors[idx],
loss_name='absolute'))
all_mtl_performances.append(np.nanmean(all_test_errors))
average_stuff = pd.DataFrame(
all_individual_cum_errors).rolling(
window=10**10, min_periods=1).mean().values.ravel()
if average_stuff[-1] < best_perf:
best_perf = average_stuff[-1]
best_mtl_performances = all_mtl_performances
best_average = average_stuff
# print('inner step: %8e | meta step: %8e | perf: %10.3f' % (inner_step_size, meta_step_size, np.nanmean(all_individual_cum_errors)))
else:
pass
# print('inner step: %8e | meta step: %8e | perf: %10.3f' % (inner_step_size, meta_step_size, np.nanmean(all_individual_cum_errors)))
return best_mtl_performances, best_average
def fit(self, data):
best_perf = np.Inf
for _, inner_step_size in enumerate(self.inner_step_size_range):
for _, meta_step_size in enumerate(self.meta_step_size_range):
all_individual_cum_errors = []
all_mtl_performances = []
all_final_weight_vectors = []
curr_metaparameter = np.zeros(data.features_tr[0].shape[1])
for task_iteration, task in enumerate(data.tr_task_indexes):
x = data.features_tr[task]
y = data.labels_tr[task]
prev_metaparameter = curr_metaparameter
temp_weight_vectors = []
all_gradients = []
# initialize the inner parameters
n_points, n_dims = x.shape
curr_untranslated_weights = np.zeros(n_dims)
shuffled_indexes = list(range(n_points))
# np.random.shuffle(shuffled_indexes)
for inner_iteration, curr_point_idx in enumerate(
shuffled_indexes):
prev_untranslated_weights = curr_untranslated_weights
# update inner weight vector
curr_weights = curr_untranslated_weights + curr_metaparameter
temp_weight_vectors.append(curr_weights)
# receive a new datapoint
curr_x = x[curr_point_idx, :]
curr_y = y[curr_point_idx]
all_individual_cum_errors.append(
loss(curr_x,
curr_y,
curr_weights,
loss_name='absolute'))
# compute the gradient
subgrad = subgradient(curr_x,
curr_y,
curr_weights,
loss_name='absolute')
full_gradient = subgrad * curr_x
all_gradients.append(full_gradient)
# update the untranslated weights
curr_untranslated_weights = prev_untranslated_weights - inner_step_size * full_gradient
# update metaparameters
curr_metaparameter = prev_metaparameter - meta_step_size * np.sum(
all_gradients, axis=0)
all_final_weight_vectors.append(
np.mean(temp_weight_vectors, axis=0))
all_test_errors = []
for idx, curr_test_task in enumerate(
data.tr_task_indexes[:task_iteration]):
all_test_errors.append(
loss(data.features_ts[curr_test_task],
data.labels_ts[curr_test_task],
all_final_weight_vectors[idx],
loss_name='absolute'))
all_mtl_performances.append(np.nanmean(all_test_errors))
average_stuff = pd.DataFrame(
all_individual_cum_errors).rolling(
window=10**10, min_periods=1).mean().values.ravel()
if average_stuff[-1] < best_perf:
best_perf = average_stuff[-1]
best_inner = inner_step_size
best_meta = meta_step_size
best_average = average_stuff
best_mtl_performances = all_mtl_performances
# plt.plot(average_stuff)
# plt.title('best inner step ' + str(best_inner) + ' | ' + 'best meta step ' + str(best_meta))
# plt.ylim(top=12, bottom=0)
# plt.pause(0.1)
# k = 1
return best_mtl_performances, best_average
def fit(self, data):
meta_wealth_range = [1]
inner_wealth_range = [1]
best_cumsum_perf = np.Inf
for meta_idx, meta_wealth in enumerate(meta_wealth_range):
for inner_idx, inner_wealth in enumerate(inner_wealth_range):
all_individual_cum_errors = []
curr_meta_fraction = self.meta_magnitude_betting_fraction
curr_meta_wealth = meta_wealth
curr_meta_magnitude = curr_meta_fraction * curr_meta_wealth
curr_meta_direction = np.zeros(data.features_tr[0].shape[1])
all_final_weight_vectors = []
all_h_meta = []
all_mtl_performances = []
all_meta_parameters = []
for task_iteration, task in enumerate(data.tr_task_indexes):
x = data.features_tr[task]
y = data.labels_tr[task]
task_iteration = task_iteration + 1
# initialize the inner parameters
n_points, n_dims = x.shape
curr_inner_fraction = self.inner_magnitude_betting_fraction
curr_inner_wealth = inner_wealth
curr_inner_magnitude = curr_inner_fraction * curr_inner_wealth
curr_inner_direction = np.zeros(n_dims)
all_h_inner = []
temp_weight_vectors = []
shuffled_indexes = list(range(n_points))
# np.random.shuffle(shuffled_indexes)
for inner_iteration, curr_point_idx in enumerate(shuffled_indexes):
inner_iteration = inner_iteration + 1
prev_meta_direction = curr_meta_direction
prev_meta_fraction = curr_meta_fraction
prev_meta_wealth = curr_meta_wealth
prev_meta_magnitude = curr_meta_magnitude
prev_inner_direction = curr_inner_direction
prev_inner_fraction = curr_inner_fraction
prev_inner_wealth = curr_inner_wealth
prev_inner_magnitude = curr_inner_magnitude
# define total iteration
total_iter = self.__general_iteration(task_iteration, n_points, inner_iteration)
# update meta-parameter
meta_parameter = prev_meta_magnitude * prev_meta_direction
all_meta_parameters.append(meta_parameter)
# update inner weight vector
weight_vector = prev_inner_magnitude * prev_inner_direction + meta_parameter
temp_weight_vectors.append(weight_vector)
# receive a new datapoint
curr_x = x[curr_point_idx, :]
curr_y = y[curr_point_idx]
all_individual_cum_errors.append(loss(curr_x, curr_y, weight_vector, loss_name='absolute'))
# compute the gradient
subgrad = subgradient(curr_x, curr_y, weight_vector, loss_name='absolute')
full_gradient = subgrad * curr_x
# define meta step size
meta_step_size = (1 / (self.L * self.R)) * np.sqrt(2 / total_iter)
# update meta-direction
curr_meta_direction = l2_unit_ball_projection(prev_meta_direction - meta_step_size * full_gradient)
# define inner step size
inner_step_size = (1 / (self.L * self.R)) * np.sqrt(2 / inner_iteration)
# update inner direction
curr_inner_direction = l2_unit_ball_projection(prev_inner_direction - inner_step_size * full_gradient)
# update meta-magnitude_wealth
curr_meta_wealth = prev_meta_wealth - (1 / (self.R * self.L)) * full_gradient @ prev_meta_direction * prev_meta_magnitude
# meta h thing
h_meta = (1 / (self.R * self.L)) * (full_gradient @ prev_meta_direction) * (1 / (1 - (1 / (self.R * self.L)) * (full_gradient @ prev_meta_direction) * prev_meta_fraction))
all_h_meta.append(h_meta)
a_thing_meta = 1 + np.sum([curr_h ** 2 for curr_h in all_h_meta])
# update meta-magnitude_betting_fraction
curr_meta_fraction = np.max([np.min([prev_meta_fraction - (2 / (2 - np.log(3))) * (h_meta / a_thing_meta), 1 / 2]), -1 / 2])
# update meta-magnitude
curr_meta_magnitude = curr_meta_fraction * curr_meta_wealth
# update inner magnitude_wealth
curr_inner_wealth = prev_inner_wealth - 1 / (self.R * self.L) * full_gradient @ prev_inner_direction * prev_inner_magnitude
# update magnitude_betting_fraction
h_inner = (1 / (self.R * self.L)) * full_gradient @ prev_inner_direction * (1 / (1 - (1 / (self.R * self.L)) * full_gradient @ prev_inner_direction * prev_inner_fraction))
all_h_inner.append(h_inner)
a_thing_inner = 1 + np.sum([curr_h**2 for curr_h in all_h_inner])
# update magnitude_betting_fraction
curr_inner_fraction = np.max([np.min([prev_inner_fraction - (2 / (2 - np.log(3))) * (h_inner / a_thing_inner), 1/2]), -1/2])
# update magnitude
curr_inner_magnitude = curr_inner_fraction * curr_inner_wealth
all_final_weight_vectors.append(np.mean(temp_weight_vectors, axis=0))
all_test_errors = []
for idx, curr_test_task in enumerate(data.tr_task_indexes[:task_iteration]):
all_test_errors.append(loss(data.features_ts[curr_test_task], data.labels_ts[curr_test_task], all_final_weight_vectors[idx], loss_name='absolute'))
all_mtl_performances.append(np.nanmean(all_test_errors))
if np.nanmean(all_individual_cum_errors) < best_cumsum_perf:
best_cumsum_perf = np.nanmean(all_individual_cum_errors)
best_cumsum_performances = all_individual_cum_errors
best_mtl_performances = all_mtl_performances
print('inner wealth: %8.2f | meta wealth: %8.2f | perf: %10.3f' % (inner_wealth, meta_wealth, np.nanmean(all_individual_cum_errors)))
else:
print('inner wealth: %8.2f | meta wealth: %8.2f| perf: %10.3f' % (inner_wealth, meta_wealth, np.nanmean(all_individual_cum_errors)))
return best_mtl_performances, pd.DataFrame(best_cumsum_performances).rolling(window=10 ** 10, min_periods=1).mean().values.ravel()
def fit(self, data, task_indexes):
all_mtl_performances = []
all_errors = []
total_points = 0
for task_idx, task in enumerate(getattr(data, task_indexes)):
x = data.features_tr[task]
y = data.labels_tr[task]
n_points, n_dims = x.shape
total_points = total_points + n_points
wealth_range = [1]
for idx, wealth in enumerate(wealth_range):
curr_bet_fraction = self.magnitude_betting_fraction
curr_wealth = wealth # self.magnitude_wealth
curr_magnitude = curr_bet_fraction * curr_wealth
curr_direction = np.random.randn(n_dims)
all_weight_vectors = []
all_h = []
shuffled_indexes = list(range(n_points))
np.random.shuffle(shuffled_indexes)
for iteration, curr_point_idx in enumerate(shuffled_indexes):
iteration = iteration + 1
prev_direction = curr_direction
prev_bet_fraction = curr_bet_fraction
prev_wealth = curr_wealth
prev_magnitude = curr_magnitude
# update weight vector
weight_vector = prev_magnitude * prev_direction + self.fixed_bias
all_weight_vectors.append(weight_vector)
# receive a new datapoint
curr_x = x[curr_point_idx, :]
curr_y = y[curr_point_idx]
all_errors.append(
loss(curr_x,
curr_y,
weight_vector,
loss_name='absolute'))
# compute the gradient
subgrad = subgradient(curr_x,
curr_y,
weight_vector,
loss_name='absolute')
full_gradient = subgrad * curr_x
# define step size
step_size = 1 / (self.L * self.R) * np.sqrt(2 / iteration)
# update direction
curr_direction = l2_unit_ball_projection(prev_direction -
step_size *
full_gradient)
# update magnitude_wealth
curr_wealth = prev_wealth - (
1 / (self.R * self.L)
) * full_gradient @ prev_direction * prev_magnitude
# h_thing
h = (
1 / (self.R * self.L)
) * full_gradient @ prev_direction * (
1 /
(1 - (1 / (self.R * self.L)) *
(full_gradient @ prev_direction) * prev_bet_fraction))
all_h.append(h)
a_thing = 1 + np.sum([curr_h**2 for curr_h in all_h])
# update magnitude_betting_fraction
curr_bet_fraction = np.max([
np.min([
prev_bet_fraction - (2 / (2 - np.log(3))) *
(h / a_thing), 1 / 2
]), -1 / 2
])
# update magnitude
curr_magnitude = curr_bet_fraction * curr_wealth
if len(all_weight_vectors) < 2:
final_w = weight_vector
else:
final_w = np.mean(all_weight_vectors, axis=0)
curr_test_perf = loss(data.features_ts[task],
data.labels_ts[task],
final_w,
loss_name='absolute')
all_mtl_performances.append(curr_test_perf)
return (task_idx + 1) * [np.nanmean(all_mtl_performances)
], total_points * [np.nanmean(all_errors)]
def fit(self, data):
all_individual_cum_errors = []
best_metaparameter = np.zeros(data.features_tr[0].shape[1])
for task_iteration, task in enumerate(data.tr_task_indexes):
x = data.features_tr[task]
y = data.labels_tr[task]
# initialize the inner parameters
n_points, n_dims = x.shape
curr_metaparameter = best_metaparameter
best_perf = np.Inf
for meta_idx, meta_step_size in enumerate(
self.meta_step_size_range):
for inner_idx, inner_step_size in enumerate(
self.inner_step_size_range):
if meta_idx == 0 and inner_idx == 0:
og_metaparameter = deepcopy(curr_metaparameter)
else:
curr_metaparameter = og_metaparameter
temp_cum_errors = []
curr_untranslated_weights = np.zeros(n_dims)
shuffled_indexes = list(range(n_points))
# np.random.shuffle(shuffled_indexes)
for inner_iteration, curr_point_idx in enumerate(
shuffled_indexes):
prev_untranslated_weights = curr_untranslated_weights
prev_metaparameter = curr_metaparameter
# update inner weight vector
curr_weights = curr_untranslated_weights + prev_metaparameter
# receive a new datapoint
curr_x = x[curr_point_idx, :]
curr_y = y[curr_point_idx]
temp_cum_errors.append(
loss(curr_x,
curr_y,
curr_weights,
loss_name='absolute'))
# compute the gradient
subgrad = subgradient(curr_x,
curr_y,
curr_weights,
loss_name='absolute')
full_gradient = subgrad * curr_x
# update metaparameters
curr_metaparameter = prev_metaparameter - meta_step_size * full_gradient
# update the untranslated weights
curr_untranslated_weights = prev_untranslated_weights - inner_step_size * full_gradient
curr_cum_sum = pd.DataFrame(temp_cum_errors).rolling(
window=10**10, min_periods=1).mean().values.ravel()
if curr_cum_sum[-1] < best_perf:
best_perf = curr_cum_sum[-1]
best_cum_errors = temp_cum_errors
best_metaparameter = deepcopy(curr_metaparameter)
print(
'%3d | best params: %10.5f | %10.5f | error: %5.3f'
% (task_iteration, inner_step_size, meta_step_size,
best_perf))
# print('inner step: %8e | meta step: %8e | perf: %10.3f' % (inner_step_size, meta_step_size, np.nanmean(all_individual_cum_errors)))
else:
pass
# print('inner step: %8e | meta step: %8e | perf: %10.3f' % (inner_step_size, meta_step_size, np.nanmean(all_individual_cum_errors)))
all_individual_cum_errors = all_individual_cum_errors + best_cum_errors
return None, pd.DataFrame(all_individual_cum_errors).rolling(
window=10**10, min_periods=1).mean().values.ravel()